System and method for traffic control in railways

ABSTRACT

The present invention relates to a system and method for traffic control in railways. The present invention also relates to a corresponding use. The present invention is particularly directed to the computation and prediction of optimal routes for a plurality of trains, and the optimization of train tracks capacity. The method comprises controlling traffic in railways, the method comprising sampling sensor data relevant to railway system via at least one sensor ( 200 ) and using at least one server ( 500 ) for receiving the sensor data from the at least one sensor ( 200 ), predicting the status of the railway infrastructure based on future rolling stock; and controlling the traffic of rolling stock on the basis of the future status.

FIELD

The invention lies in the field of traffic control. The invention isapplicable in the field of traffic control in railways and thereforerelates to a system and method for traffic control in railways. Theinvention is particularly directed to the computation and prediction ofoptimal routes for a plurality of trains. The invention further relatesto the optimization of train tracks capacity.

INTRODUCTION

Railroad, railway or rail transport has been developed for transferringgoods and passengers on wheeled vehicles on rails, also known as tracks.In contrast to road transport, where vehicles run on a prepared flatsurface, rail vehicles (rolling stock) are directionally guided by thetracks on which they run. Tracks commonly consist of steel rails,installed on ties or sleepers and ballast, on which the rolling stock,usually provided with metal wheels, moves. Other variations are alsopossible, such as slab track, where the rails are fastened to a concretefoundation resting on a subsurface.

Rolling stock in a rail transport system generally encounters lowerfrictional resistance than road vehicles, so passenger and freight cars(carriages and wagons) can be coupled into longer trains. Power isprovided by locomotives, which either draw electric power from a railwayelectrification system or produce their own power, usually by dieselengines. Most tracks are accompanied by a signaling system. Railways area safe land transport system when compared to other forms of transport.Additionally, railways are capable of high levels of passenger and cargoutilization and energy efficiency but are often less flexible and morecapital-intensive than road transport, when lower traffic levels areconsidered.

The inspection of railway equipment is essential for the safe movementof trains. Many types of defect detectors are in use today. Thesedevices utilize technologies that vary from a simplistic paddle andswitch to infrared and laser scanning, and even ultrasonic audioanalysis. Their use has avoided many rail accidents over the pastdecades.

Another essential aspect for the safe movement of trains in the controlof traffic in railways. A plurality of factors and parameters play arole in the occurrence of different scenarios in railways, there thetraffic in railways must keep up with intensive supervision in order tominimize incents, which may result in enormous economically and humanloss. Additionally, a poor traffic control may also aggravate the effectof infrastructure failures on the traffic in railways and it may, forexample, disrupt freight revenue operations and passenger services.

Unlike highways or road networks where capacity is disaggregated intounlinked trips over individual route segments, railway capacity isfundamentally considered a network system. As a result, many componentscan cause system disruptions. Traffic control of railways mustacknowledge the vast array of a route's performance such as, forexample, type of train service, number of tracks, types of traincontrol, trains speeds, wear effect on tracks, pick hours in the railwaynetwork and priority of circulation.

Railway operations require careful monitoring and control of the trafficto ensure passenger safety and reliable service. Many sensors are usedto monitor and obtain data from the traffic in railways, which may beused to ensure the integrity of the service and identify possiblesources of malfunction. Such sensors allow for data collection andanalysis and ensure safer traffic of railways. Various sensors can beplaced directly on trains, on tracks or nearby, at train stations and/oron platforms, and generally in the overall vicinity of the railway.Measurements of such sensors may be used to further measurements,control, prediction and optimization of traffic control in railways.Several different attempts have been made to implement systems andmethods for train traffic control.

For instance, U.S. Pat. No. 6,179,252 B1 discloses an invention relatedto an intelligent intersection control system that features an internalcontroller that receives digital messages containing detailedinformation items concerning, for example, the direction, speed, lengthand identity of a train. The controller generates appropriate commandsthat coordinate the functions of crossing safety devices. A controlleris capable of receiving and using much more detailed train informationthan is possible with conventional warning systems. Railroad crossingwarning features are capable of responding more flexibly to this moredetailed train information. The controller also continuously adjusts theactivation state for safety devices associated with the crossing. Inparticular embodiments, the control system provides and displayscrossing status information including the amount of time remaining untila crossing is cleared of train traffic, the approach of a second trainduring blocking of the crossing by a first train, or a suggestedalternate route for waiting road vehicles. The controller may also beused to actuate numerous standard crossing warning features, includingcrossing blocking arms, flashing lights, warning chimes and warninghorns.

Furthermore, the U.S. Pat. No. 5,950,966 A discloses a system forcontrolling train movement that uses a distributed architecture. Waysidecontrollers receive signals from individual trains, including positioninformation derived from a navigation system. The wayside controllersinterface with a central train control network and coordinate localtrain movement including the issuance of incremental authorities.

Moreover, there have been inventions proposition an automatic vehiclecontrol and location system. For example, U.S. Pat. No. 5,364,047 Adiscloses a signaling and traffic control system, which is capable of avehicle determining its own absolute position along a guideway based oninformation received from the wayside using an inductive loop or beaconsystem in conjunction with the distance traveled according to theonboard tach generator(s), and report its position to a wayside controldevice, whereby the wayside control device reports to the vehicle, aspart of its communications message, the location of the closest forwardobstacle. Based upon this information, the vehicle controls itselfsafely based upon its characteristics as contained in a topographicaldatabase and a vehicle database. This train control system is atrain-oriented block system (i.e., moving block). The system requiresvital two-way data communication between the wayside and the vehicle,and between adjacent control sectors.

Additionally, U.S. Pat. No. 5,332,180 A discloses a railway trafficcontrol system in which accurate vehicle information is effectivelyavailable in real-time to facilitate control of traffic flow. Unlikeprior art methods of precisely monitoring train location, the currentinvention is dependent only on equipment onboard the vehicle andposition updates provided by external benchmarks located along the trackroute. The system's dynamic motion capabilities can also be used tosense and store track rail signatures, as a function of rail distance,which can be routinely analyzed to assist in determining rail androad-bed conditions for preventative maintenance purposes. In presentlypreferred embodiments, the on-board vehicle information detectionequipment comprises an inertial measurement unit providing dynamicvehicle motion information to a position processor. Depending on theamount and quality of a priori knowledge of the vehicle route, theinertial measurement unit may have as many as three gyroscopes and threeaccelerometers or as little as a single accelerometer. To minimize errorbetween benchmarks, the processor preferably includes a recursiveestimation filter to combine the a priori route information withmovement attributes derived from the inertial measurement unit.

SUMMARY

In light of the above, it is therefore an object of the presentinvention to overcome or at least to alleviated the shortcomings anddisadvantages of the prior art. More particularly, it is an object ofthe present invention to provide a system and method for traffic controlin railways. It is also a preferred object of the present invention todisclose a controlling, monitoring and processing system for sensor datarelevant to traffic in a plurality of railways.

These objects are met by the present invention.

The present invention relates to a method and a system for controllingtraffic in railways. In a first embodiment the method may comprisesampling sensor data relevant to railway system via at least one sensor.

The term sensor is intended to comprise at least one device, module,model and/or subsystem whose purpose is to detect parameters and/orchanges in its environment and provides a respective signal to otherdevices. It will be understood that the term “different kind of sensor”is intended to mean sensors that are configured to measure differentparameters or the same parameters with different technologies. Anexample for the latter is lasers or induction loops, both provided tomeasure speed.

Furthermore, the term railway system is intended to comprise railwayinfrastructure and rolling stocks. The term railway infrastructure isintended to comprise railway tracks, trackage, permanent ways,electrification systems, sleepers or crossties, tracks, rails,rail-based suspension railways, switches, frogs, point machines,crossings, interlockings, turnouts, masts, signaling equipment,electronic housings, buildings, tunnels, railway, stations and/orinformational and computational network. It will also be understood thatit basically comprises any position on a railway network where a sensormay be placed, and which may allow sampling sensor data containinginformation that may be directly or indirectly relevant to the railwaytraffic. Furthermore, the term rolling stock is intended to comprise anyvehicle(s) moving on a railway, wheeled vehicles, powered and unpoweredvehicles, such as for example, locomotives, railroad cars, coaches,wagons, construction site vehicles, draisines and/or trolleys.

The invention may further provide using at least one server which may beconfigured for receiving the sensor data from the at least one sensor,predicting the status of the railway infrastructure based on futurerolling stock, and controlling the traffic of rolling stock on the basisof the future status. It will be understood that the prediction and/orpredicting is intended to mean predictive analytics that encompasses avariety of statistical techniques from predictive modelling, machinelearning, and data mining that analyze current and historical facts tomake predictions about future or otherwise unknown events. It will alsobe understood that the term server may also refer to a computer program,and/or a device, and/or a plurality of each or both that may providefunctionality for other programs, devices and/or components of thepresent invention. For instance, a server may provide variousfunctionalities, which may be referred to as services, such as, forexample, sharing data or resources among multiple clients, or performingcomputation and/or storage functions. It will further be understood thata single server may serve multiple clients, and a single client may usemultiple servers. Furthermore, a client process may run on the samedevice or may connect over a network to a server on a different device,such as a remote server or a cloud. The server may have rather primitivefunctions, such as just transmitting rather short information to anotherlevel of infrastructure, or can have a more sophisticated structure,such as a storing, processing and transmitting unit.

In one embodiment of the present invention, the method may furthercomprise processing sensor data to generate processed sensor data,analyzing the processed sensor data to obtain information relevant torailway system, using the obtained relevant information for planning therouting of rolling stocks, and transmitting the route planning to atleast one of server and/or at least one authorized user. The routing ofrolling stocks may be based on at least one of the following current orpredicted relevant information of railways: technical condition ofassets (i.e. technical condition of railway infrastructure, which alsomay comprise the effect caused by the infrastructure on the rollingstocks), degrading effect of rolling stocks (i.e. the effect caused bythe rolling stock on the infrastructure), traffic load information ofrolling stocks, risks of traffic delay, unplanned and/or plannedmaintenance and/or inspections, maintenance effectiveness metrics; andweather information.

Furthermore, the method may comprise subjecting the relevantinformation, obtained from the sensor data, to analysis via at least oneanalytical approach, each approach comprising at least one of signalfilter processing, pattern recognition, probabilistic modeling, Bayesianschemes, machine learning, supervised learning, unsupervised learning,reinforcement learning, statistical analytics, statistical models,principle component analysis, independent component analysis (ICA),dynamic time warping, maximum likelihood estimates, modeling,estimating, neural network, convolutional network, deep convolutionalnetwork, deep learning, ultra-deep learning, genetic algorithms, Markovmodels, and/or hidden Markov models.

Moreover, the method routing the rolling stocks is based on at least oneanalytical approach, each approach comprising at least one of signalfilter processing, pattern recognition, probabilistic modeling, Bayesianschemes, machine learning, supervised learning, unsupervised learning,reinforcement learning, statistical analytics, statistical models,principle component analysis, independent component analysis (ICA),dynamic time warping, maximum likelihood estimates, modeling,estimating, neural network, convolutional network, deep convolutionalnetwork, deep learning, ultra-deep learning, genetic algorithms, Markovmodels, and/or hidden Markov models. It will be understood that this isnot an exhaustive list, and it will also be understood that that, forexample, a first analytical approach may differ from, for example, asecond analytical approach. Further, it will be understood that the termanalytical approach is intended to comprise any analytical tool that isused to analyze signals or data, and it may also be referred to asanalytical method. These analytical methods can be applied alone or anycombination thereof, sequentially and/or in parallel. Differentanalytical approaches can thus be different in the kind of one or moreanalytical method(s) and/or just the order of a plurality of analyticalmethods when even using the same methods but just in a different order.

In one embodiment of the present invention, the method may furthercomprise associating and/or arranging at least one sensor with at leastone of rolling stock and/or railway infrastructure. It will beunderstood that this is not an exhaustive list of objects the sensorsmay be releasable or fixedly assembled to. It will be also understoodthat rail infrastructure may comprise a switch component, which may,inter alia, but limited to, comprise at least one of a sleeper, a frog,a point machine, switch frog, switch blade and/or an interlocking forparticularly measuring point machine current at the interlocking.

The method may also comprise using the server for providing at least onesignal comprising: parameters to define the route of rolling stocks,prediction of railway traffic, prediction of wear effect of rollingstocks on the railway infrastructure. Additionally or alternatively, themethod may base at least one signal on at least one analytical approach,each approach comprising at least one of signal filter processing,pattern recognition, probabilistic modeling, Bayesian schemes, machinelearning, supervised learning, unsupervised learning, reinforcementlearning, statistical analytics, statistical models, principle componentanalysis, independent component analysis (ICA), dynamic time warping,maximum likelihood estimates, modeling, estimating, neural network,convolutional network, deep convolutional network, deep learning,ultra-deep learning, genetic algorithms, Markov models, and/or hiddenMarkov models. Furthermore, the signal may be transmitted via wirelesslyand/or hard-wired means. Moreover, the invention may provide the furtherstep of associating, for example, a first and a second sensor data toobtain relevant information to railway traffic, which may beadvantageous, as it may allow to determine if first sensor data has adirectly or indirectly influence or has an impact to the secondanalytical data and/or vice versa.

The method may further comprise at least one of: contrasting the routeplanning with the current traffic in railways, providing and receivingfeedback of current traffic in railways, and/or providing instructionfor controlling the traffic in railways. Additionally or alternatively,the method may comprise the step of (semi)automated controlling routingof rolling stocks according to their wear effect on the railways. Itwill be understood that whenever the term (semi)automated is used, theautomation of the step, process, and/or is a preferred, for example, themethod may comprise the step of (semi)automated controlling routing ofrolling stocks, where the method may comprise the step of preferablyautomated controlling routing of rolling stocks.

For instance, the invention may predict the future status of the trafficin railways by evaluating the health condition of any asset involved.For this, multiple sources to derive a health status that reflects theactual usage of the asset may be used. As an example, the stress and,hence, the wear of the frog (crossing point of two rails) is mainly theresult of trains running over it and the temperature changes over time.The invention uses the continuously recorded and combined data to derivethe stress and to accumulate it over time. In contrast to the state ofthe art, this stress is calculated taking into account the train type,speed, vibration power, temperature, direction of travel of each passingtrain which gives a reflects the wear much more accurate than a generalestimated number of gross tons passing the asset, which may beadvantageous, as it may provide information for optimizing the routeplanning of rolling stocks. It will be understood that the termoptimizing and/or optimization is intended to comprise the (semi)automated selection of a best available element (with regard to somecriterion) from some set of available alternatives. It can be the bestvalue(s) of some objective function given a defined domain (or input),including a variety of different types of objective functions anddifferent types of domains.

Moreover, the invention may use the sensor data for generating signalsfor processing and/or methods of machine learning and artificialintelligence (AI) to derive information like vertical movement,vibration, train speed, train type from multiple data sources. Thus, theinvention may be able to plan the routing of rolling stock based on datarelating, for example, train categories (high-speed, passenger, cargotrains) using vendor specific train “footprints” to aggregate anaccurate usage statistic and detect specific attributes of a train (e.g.so called “flat wheels”) which induce higher wear and abrasion on therailroad infrastructure. The invention may associate identified rollingstocks to an optimal routing.

Furthermore, in one embodiment of the present invention, the method mayalso comprise using the information sampled via at least one sensor toretrieve information of at least one sensor data measurements, forexample, but no limited to, length, mass, time, current, electrictension, temperature, humidity, luminous intensity and any parametersderived therefrom such as acceleration, vibration, speed, time,distance, illumination, images, gyroscopic information, acoustics,ultra-sound, air pressure, magnetism, electro-magnetism, position,optical sensor information, etc.

Even further, the method may comprise the step of (semi)automatedcontrolling the traffic and/or routing of rolling stocks in railways.For this purpose, the method may use a continuous data transmission. Inanother embodiment, the method may also use a periodical datatransmission.

In another embodiment of the present invention, the method may includethe step of storing all data generated by the at least one server.

A system according to the present invention may particularly comprise atleast one sensor configured to sample sensor data relevant to railwaysystem. Furthermore, the system may also comprise at least one serverconfigured to receive the sensor data from the sensor, predict thefuture status of the railway infrastructure based on future rollingstock, and control the traffic of rolling stock on the basis of thefuture status.

The system may further comprise at least one sensor data processingcomponent configured to generate processed sensor data and at least oneanalyzing component configured to analyze the processed sensor data togenerate a rolling stock routing plan. In some instances, this may beadvantageous, as it may allow to the analyzing component to associatethe processed sensor data to the routing plan of rolling stocks on thebasis of, for example, a first and a second sensor data, i.e. it mayallow to the analyzing component to associate the traffic status ofrailways based on current, past or future status of rolling stocks andrailway infrastructure.

The analyzing component may be anything that is configured to providethe associated data and may comprise local and/or remote componentsand/or sub-components. It will be understood that the term associateddata is intended to comprise at least two data sets that influence theother. One data set can influence the other data set and/or theyinfluence each other and/or influence the merged data and/or influencedata derived from the merged data set. Just accumulated data is notintended to be comprised. Non-limiting examples can be one data set(e.g., comprising train specific data) merged with another data set(e.g., comprising vibration data) provide a result considering both datasets.

In one embodiment of the present invention, the system may also compriseat least one transmitting component configured to transmit the routeplanning to at least one server and/or at least one authorized userthrough an interface.

Moreover, the at least one analyzing component of the system maygenerate a rolling stock routing plan based on at least one of thefollowing current or predicted relevant information of railways:technical condition of assets, technical condition of infrastructurecomponents, degrading effect of rolling stocks, traffic load informationof rolling stocks, risks of traffic delay, unplanned and/or plannedmaintenance and/or inspections, maintenance effectiveness metrics; andweather information.

In another embodiment of the present invention, the system the at leastone analyzing component of the system may further comprise at least oneanalytical approach, each approach comprising at least one of signalfilter processing, pattern recognition, probabilistic modeling, Bayesianschemes, machine learning, supervised learning, unsupervised learning,reinforcement learning, statistical analytics, statistical models,principle component analysis, independent component analysis (ICA),dynamic time warping, maximum likelihood estimates, modeling,estimating, neural network, convolutional network, deep convolutionalnetwork, deep learning, ultra-deep learning, genetic algorithms, Markovmodels, and/or hidden Markov models.

Even further, in one embodiment of the present invention, the server ofthe system may be configured to optimize the routing of rolling stocksbased on at least one analytical approach, each approach comprising atleast one of signal filter processing, pattern recognition,probabilistic modeling, Bayesian schemes, machine learning, supervisedlearning, unsupervised learning, reinforcement learning, statisticalanalytics, statistical models, principle component analysis, independentcomponent analysis (ICA), dynamic time warping, maximum likelihoodestimates, modeling, estimating, neural network, convolutional network,deep convolutional network, deep learning, ultra-deep learning, geneticalgorithms, Markov models, and/or hidden Markov models.

The system may further comprise the association and/or arrangement atleast one sensor with at least one of rolling stock and/or railwayinfrastructure.

The system may retrieve relevant information from sampled sensor datavia at least one sensor wherein such sampled data may provideinformation of at least one of sensor data measurements, for example,but no limited to, length, mass, time, current, electric tension,temperature, humidity, luminous intensity and any parameters derivedtherefrom such as acceleration, vibration, speed, time, distance,illumination, images, gyroscopic information, acoustics, ultra-sound,air pressure, magnetism, electro-magnetism, position, optical sensorinformation etc.

In one embodiment of the present invention, the system may even furthercomprise the server being configured to provide at least one signalcomprising parameters to define the route of rolling stocks, predictionof railway traffic and prediction of wear effect of rolling stocks onthe railway infrastructure. Additionally or alternatively, the at leastone signal may be based on at least one analytical approach, eachapproach comprising at least one of signal filter processing, patternrecognition, probabilistic modeling, Bayesian schemes, machine learning,supervised learning, unsupervised learning, reinforcement learning,statistical analytics, statistical models, principle component analysis,independent component analysis (ICA), dynamic time warping, maximumlikelihood estimates, modeling, estimating, neural network,convolutional network, deep convolutional network, deep learning,ultra-deep learning, genetic algorithms, Markov models, and/or hiddenMarkov models.

The system may further comprise the at least one sensor being configuredto perform in a plurality of operation modes, and wherein the operationmodes can be configured to monitor a plurality of sensor data relevantto railway system.

In another embodiment of the present invention, the system may comprisean interface component configured to bidirectionally communicate the atleast one server with at least one authorized user.

Additionally or alternatively, the system may comprise at least oneserver being configured to monitor and predict the traffic of rollingstocks in railways taking into consideration the future status ofrailway infrastructure maintenance, which further be based on at leastone analytical approach, each approach comprising at least one of signalfilter processing, pattern recognition, probabilistic modeling, Bayesianschemes, machine learning, supervised learning, unsupervised learning,reinforcement learning, statistical analytics, statistical models,principle component analysis, independent component analysis (ICA),dynamic time warping, maximum likelihood estimates, modeling,estimating, neural network, convolutional network, deep convolutionalnetwork, deep learning, ultra-deep learning, genetic algorithms, Markovmodels, and/or hidden Markov models.

The system may also comprise at least one storing component configuredto store all data generated by the at least one server.

Furthermore, the invention may also comprise the use of the methodembodiments and/or the system embodiments in traffic control. Evenfurther, the invention may comprise the use of the method embodimentsand/or the system embodiments for controlling traffic in railways.

The present technology is also defined by the following numberedembodiments.

Below, method embodiments will be discussed. These embodiments areabbreviated by the letter “M” followed by a number. When reference isherein made to a method embodiment, those embodiments are meant.

M1. A method for controlling traffic in railways.

M2. The method according to the preceding embodiment comprising samplingsensor data relevant to railway system via at least one sensor (200).

M3. The method according to any of preceding embodiments furthercomprising using at least one server (500).

M4. The method according to any of preceding embodiments wherein the atleast one server comprises

-   -   receiving the sensor data from the at least one sensor (200);    -   predicting the status of the railway infrastructure based on        future rolling stock; and    -   controlling the traffic of rolling stock on the basis of the        future status.

M5. The method according to embodiment M1 or M4 wherein the methodfurther comprises

-   -   processing sensor data to generate processed sensor data;    -   analyzing the processed sensor data to obtain information        relevant to railway system;    -   using the obtained relevant information for planning the routing        of rolling stocks;    -   transmitting the route planning to at least one of server and/or        at least one authorized user.

M6. The method according to the preceding embodiment wherein the routingof rolling stocks is based on at least one of the following current orpredicted relevant information of railways:

-   -   technical condition of assets;    -   degrading effect of rolling stocks;    -   degrading effect of assets;    -   traffic load information of rolling stocks;    -   risks of traffic delay;    -   unplanned maintenance and/or inspections;    -   planned maintenance and/or inspections;    -   maintenance effectiveness metrics; and    -   weather information.

M7. The method according to any of the preceding embodiments and withfeatures of embodiment M5 wherein the relevant information obtained fromthe sensor data is subjected to analysis via at least one analyticalapproach.

M8. The method according to any of the preceding embodiments and withfeatures of embodiment M5 wherein the routing of rolling stocks is basedon at least one analytical approach.

M9. The method according to any of the preceding two embodiments whereinthe method further comprises associating and/or arranging at least onesensor with at least one of rolling stock and/or railway infrastructure.

M10. The method according to any of the preceding embodiments whereinthe method further comprises using the server (500) for providing atleast one signal comprising

-   -   parameters to define the route of rolling stocks;    -   prediction of railway traffic;    -   prediction of wear effect of rolling stocks on the railway        infrastructure; and

wherein the at least one signal is based on at least one analyticalapproach.

M11. The method according to any of the preceding embodiments furtherwherein the method further comprises at least one of

-   -   contrasting the route planning with the current traffic in        railways;    -   providing and receiving feedback of current traffic in railways;        and/or    -   providing instruction for controlling the traffic in railways.

M12. The method according to any of preceding embodiments furthercomprising using the sensor data for generating signals for processingand/or methods of machine learning and artificial intelligence (AI) toderive information like vertical movement, vibration, train speed, traintype from multiple data sources.

M13. The method according to any of the preceding embodiments whereinfurther comprising the step of (semi)automated controlling the trafficand/or routing of rolling stocks in railways.

M14. The method according to any of the preceding embodiments whereinthe step of (semi)automated controlling routing of rolling stocks arebased on the wear effect of rolling stocks on the railways.

M15. The method according to any of the preceding embodiments comprisesusing the information sampled via at least one sensor to retrieveinformation of at least one sensor data measurements.

M16. The method according to any of the preceding embodiments and withfeatures of embodiment M7 wherein the data transmission is continuous.

M17. The method according to any of the embodiments M1 to M14 and withfeatures of embodiment M7 wherein the data transmission is periodical.

M18. The method according to any of the preceding embodiments whereinthe method further comprises storing all data generated by the at leastone server.

Below, system embodiments will be discussed. These embodiments areabbreviated by the letter “S” followed by a number. When reference isherein made to a system embodiment, those embodiments are meant.

S1. A system for controlling traffic (100) in railways.

S2. The system according to the preceding embodiment comprising at leastone sensor (200) configured to sample sensor data relevant to railwaysystem.

S3. The system according to embodiment S1 or S2, the system furthercomprising at least one server (500) configured to

-   -   receive the sensor data from the sensor (200);    -   predict the future status of the railway infrastructure based on        future rolling stock; and    -   control the traffic of rolling stock on the basis of the future        status.

S4. The system according to according to embodiment S2 or S3 wherein thesystem further comprises at least one sensor data processing component(300) configured to generate processed sensor data.

S5. The system according to according to the preceding embodimentwherein the system further comprises at least one analyzing component(400) configured to analyze the processed sensor data to generate arolling stock routing plan.

S6. The system according to according to the preceding embodimentwherein the system further comprises at least one transmitting component(800) configured to transmit the route planning to at least one server(500) and/or at least one authorized user through an interface (700).

S7. The system according to the preceding embodiment wherein the atleast one analyzing component (400) generates a rolling stock routingplan based on at least one of the following current or predictedrelevant information of railways:

-   -   technical condition of assets;    -   degrading effect of rolling stocks;    -   degrading effect of assets;    -   traffic load information of rolling stocks;    -   risks of traffic delay;    -   unplanned maintenance and/or inspections;    -   planned maintenance and/or inspections;    -   maintenance effectiveness metrics; and    -   weather information.

S8. The system according to any of the two preceding embodiments whereinthe at least one analyzing component (400) further comprises at leastone analytical approach.

S9. The system according to any of the preceding system embodimentswherein the server (500) is configured to optimize the routing ofrolling stocks based on at least one analytical approach.

S10. The system according to any of the preceding system embodimentsfurther comprising the association and/or arrangement at least onesensor (200) with at least one of rolling stock and/or railwayinfrastructure.

S11. The system according to any of the preceding system embodimentswherein the information sampled via at least one sensor (200) provideinformation of at least one sensor data measurements.

S12. The system according to any of the preceding system embodimentswherein the server (500) is configured to provide at least one signalcomprising parameters to define the route of rolling stocks;

-   -   prediction of railway traffic;    -   prediction of wear effect of rolling stocks on the railway        infrastructure; and

wherein the at least one signal is based on at least one analyticalapproach.

S13. The system according to any of the preceding system embodimentswherein the at least one sensor (200) is configured to perform in aplurality of operation modes, and wherein the operation modes can beconfigured to monitor a plurality of sensor data relevant to railwaysystem.

S14. The system according to any of the preceding system embodimentswherein the server (500) comprises an interface component (700)configured to bidirectionally communicate the at least one server withat least one authorized user.

S15. The system according to any of the preceding system embodimentswherein the server (500) further comprises monitoring and predicting thetraffic of rolling stocks in railways considering the future status ofrailway infrastructure maintenance based on at least one analyticalapproach.

S16. The system any of the preceding system embodiments wherein thesystem further comprises at least one storing component (600) configuredto store all data generated by the at least one server (500).

Below, use embodiments will be discussed. These embodiments areabbreviated by the letter “U” followed by a number. When reference isherein made to a use embodiment, those embodiments are meant.

U1. Use of the method according to any of the preceding methodembodiments and/or the system according to any of the preceding systemembodiments in traffic control.

U2. Use of the method according to any of the preceding methodembodiments and/or the system according to any of the preceding systemembodiments for controlling traffic in railways.

The present invention will now be described with reference to theaccompanying drawings which illustrate embodiments of the invention.These embodiments should only exemplify, but not limit, the presentinvention.

FIG. 1 depicts a schematic example of a set-up of a plurality of sensorsto a railway infrastructure in accordance with the present invention;

FIG. 2 depicts a schematic of system for controlling traffic in railwaysaccording to embodiments of the present invention;

FIG. 3 depicts an exemplary application of the traffic control systemaccording to embodiments of the present invention;

It is noted that not all the drawings carry all the reference sings.Instead, in some of the drawings, some of the reference sings have beenomitted for sake of the brevity and simplicity of illustration.Embodiments of the present invention will now be described withreference to the accompanying drawings.

FIG. 1 schematically depicts a description of a system configured for arailway infrastructure. In simple terms, the system may comprise arailway section with the railway 1 itself, comprising rails 2 andsleepers 3. Instead of the sleepers 3 also a solid bed for the rails 2can be provided.

Moreover, a further example of constitutional elements is conceptuallyrepresented a mast, conceptually identified by reference numeral 4. Suchconstitutional elements are usually arranged at or in the vicinity ofrailways. Furthermore, a tunnel is shown, conceptually identified byreference numeral 5. It is needless to say that other constructions,buildings etc. may be present and also used for the present invention asdescribed before and below.

For instance, a first sensor 10 can be arranged on one or more of thesleepers. The sensor 10 can be an acceleration sensor and/or any otherkind of railway specific sensor. Examples have been mentioned before.

Further, a second sensor 11 can also arranged on another sleeper distantfrom the first sensor 10. Although it seems just a small distance in thepresent example, those distances can range from the distance to theneighboring sleeper to one or more kilometers. Other sensors can be usedfor attachment to the sleepers as well. The sensors can further be ofdifferent kind—such as where the first sensor 10 may be an accelerationsensor, the second sensor 11 can be a magnetic sensor or any othercombination suitable for the specific need. The variety of sensors areenumerated before.

Another sensor 20, which may be different or the same kind of sensor,can be attached, for example, to the mast 4 or any other structure. Thismay be a different kind of sensor, such as, for example, an optical,temperature, even acceleration sensor, etc. A further kind of sensor,for example sensor 30, can be arranged above the railway as at thebeginning or within the tunnel 5. This could, for example, be a heightsensor for determining the height of a train, an optical sensor, adoppler sensor etc. It will be understood that all those sensorsmentioned here and/or before are just non-limiting examples.

Furthermore, the sensors can be configured to submit the sensor data viaa communication network, such as a wireless communication network. Asthe communication network bears several advantages and disadvantagesregarding availability, transmittal distance, costs etc. the transmittalof sensor data is optimized as described herein before and below. FIG. 2schematically depicts a system 100 for controlling traffic in railways.The system 100 may comprise at least one data gathering component,identified with reference numeral 200. It will be understood that thedata gathering component 200 may comprise a plurality of sensors, asensor system or a plurality of sensor systems. Therefore, the gatheringdata component 200 may also be referred to as plurality of sensors 200,plurality of sensor systems 200, sensor system 200, sensors 200 orsimply as sensor 200. The sensors 200 may be configured to sampleinformation relevant to the traffic in railways, for instance, thevibration due to a rolling stock passing through a given track.

Furthermore, the system 100 may also comprise a processing component300. The processing component 300 may comprise a standalone componentconfigured to receive information from the sensors 200. In simple words,the processing component 300 may assume a configuration that allows itbidirectionally communicating with the sensor 200.

In one embodiment, the processing component 300 may also be integratedwith at least one of the sensors 200. In order words, the processingcomponent 300 may also comprise an imbedded module of the sensors 200.

In one embodiment of the present invention the processing component maycommunicate with an analyzing component, conceptually identified byreference numeral 400. The analyzing component 400 may be configured toprocess sensor data based on at least one analytical approach, eachapproach comprising at least one of signal filter processing, patternrecognition, probabilistic modeling, Bayesian schemes, machine learning,supervised learning, unsupervised learning, reinforcement learning,statistical analytics, statistical models, principle component analysis,independent component analysis (ICA), dynamic time warping, maximumlikelihood estimates, modeling, estimating, neural network,convolutional network, deep convolutional network, deep learning,ultra-deep learning, genetic algorithms, Markov models, and/or hiddenMarkov models.

Moreover, the analyzing component may communicate with a datatransmitting component, conceptually identified with reference numeral800. The data transmitting component 800 may comprise one or moremodules configured to receive information from the analyzing component400 and further send the received the information to a server,conceptually identified by reference numeral 500. The data transmittingcomponent 800 may also be referred to as transmitter 800.

In another embodiment of the presentation invention, the sensor 200, theprocessing component 300, the analyzing component 400 and the datatransmitting component 800 may comprise an integrated module configuredto execute subsequently the tasks corresponding to each individualcomponent. In simple words, in one embodiment the sensor 200, theprocessing component 300, the analyzing component 400 and thetransmitter 800 may comprises modules of a single component.

The data transmitting component 800 may be configured to establish abidirectional communication with the server 500. In other words, theserver 500 may retrieve information from the data transmitting component800, and further may provide information to the transmitter 800, forexample, operation parameters. It will be understood that each componentmay receive a plurality of operation parameters, for instance, theprocessing component 300 may be commanded to execute a preprocessing ofthe data received from the sensors 200. Alternatively or additionally,the processing component 200 may be instructed to transmit the originaldata received from the sensors 200, i.e. the data coming from thesensors 200 can be transferred directly to the next component withoutexecuting any further task. It will be understood that the component mayalso be configured to perform a plurality of tasks at the same time,e.g. processing the data coming from the sensor 200 before transferringto the next component and transferring the data coming from the sensors200 without any processing.

In one embodiment, the server 500 may comprise a cloud server, a remoteserver and/or a collection of different type of servers. Therefore, theserver 500 may also be referred to as cloud server 500, remote server500, or simple as servers 500. In another embodiment, the servers 500may also converge in a central server.

It will be understood that the server 500 may also be in bidirectionalcommunication with a storing component and an interface component,conceptually identified by reference numerals 600 and 700, respectively.

The storing component 600 may be configured to receive information fromthe server 500 for storage. In simple words, the storing component 600may store information provided by the servers 500. The informationprovided by the server 500 may include, for example, but not limited to,data obtained by sensors 200, data processed by the processing component300 and any additional data generated in the servers 500. It will beunderstood that the servers 500 may be granted access to the storingcomponent 600 comprising, inter alia, the following permissions, readingthe data allocated in the storing component 600, writing and overwritingthe data stored in the storing component 600, control and modify thestorage logic and the data distribution within the storing component600.

In one embodiment of the present invention the server 500 may beconfigured transmit a signal to other component of the railway systembased upon traffic information retrieved from sensors 200. For instance,a giving traffic data is provided by the server 500 and subsequently theserver 500 generates a signal containing instructions, which aretransmitted to the railway system for implementation. The set ofinstructions may comprise, inter alia, train switching from on track toanother to allow another train to continue its route. Furthermore, thesignal may be based on at least one analytical approach, each approachcomprising at least one of signal filter processing, patternrecognition, probabilistic modeling, Bayesian schemes, machine learning,supervised learning, unsupervised learning, reinforcement learning,statistical analytics, statistical models, principle component analysis,independent component analysis (ICA), dynamic time warping, maximumlikelihood estimates, modeling, estimating, neural network,convolutional network, deep convolutional network, deep learning,ultra-deep learning, genetic algorithms, Markov models, and/or hiddenMarkov models.

The interface component 700 may comprise a bidirectionally communicatedcomponent configured to exchange information with the servers 500. Inone embodiment, the interface component 700 may comprise a plurality ofsoftware interfaces with different levels, for instance, it may comprisethe front end of a dedicated software running, controlling and/orimproving railway traffic. In another embodiment, the interfacecomponent 700 may also comprise a physical terminal for providing accessto the servers 500 to an authorized user. Furthermore, the interfacecomponent 700 may be configured to facilitate providing instructions tothe server 500 and/or for requesting information from the server 500,such as, for example, a traffic data obtained by the sensor 200. Suchrequests and/or information set may be referred to as query.

The system 100 may be applied to control the traffic in a railwaynetwork. For instance, a railway network may consist of a plurality oftracks, which may also be referred to as permanent way. FIG. 3 depictsan example of a section 1000 of a railway network, on which trains A andB may be circulating through tracks, for example, 1 and 2, which mayalso be connected through a switch 3. The connecting switch 3 may assumea configuration that allow a passage from one track to any other trackin the section of the network, for instance, a passage through theconnecting switch 3 from track 1 to track 2 and/or vice versa. Theactivation of the switch 3 may be controlled by the server 500, whichmay provide operation instructions based on the traffic data obtainedfrom the sensors 200.

In one embodiment, the sensors 200 may, inter alia, adopt aconfiguration that allows identifying trains, their speeds and theirwear effect on the tracks. The data gathered by the sensors 200 mayconstitute the basis for the server 500 to generate instructions for theactivation of the switches. In simple words, if a train is approachingthis part of the network, the sensors 200 may retrieve data that mayallow activating the switches in order to redirect the trains, forexample, from track 1 to track 2, according to their speed and/or weareffect. The data gathered by the sensors 200 may be communicated to theserver 500, which may subsequently transmit the information and thecorresponding instructions to the nearest assets, for example, thenearest switch, which may consequently be activated to control thetraffic on the tracks. Furthermore, in one embodiment of the presentinvention, the system 100 may calculate the wear effect of a particularapproaching rolling stock on an individual switch of a given section ofthe network based on at least one analytical approach, each approachcomprising at least one of signal filter processing, patternrecognition, probabilistic modeling, Bayesian schemes, machine learning,supervised learning, unsupervised learning, reinforcement learning,statistical analytics, statistical models, principle component analysis,independent component analysis (ICA), dynamic time warping, maximumlikelihood estimates, modeling, estimating, neural network,convolutional network, deep convolutional network, deep learning,ultra-deep learning, genetic algorithms, Markov models, and/or hiddenMarkov models.

In another embodiment of the present invention, the system 100 maydetermine that a particular rolling stock, for example train A, may havea higher wear effect on an already more worn switches 1.4 and 2.4 ofpassage 3 and therefore may reroute the rolling stock, for example,through switches 1.5 and 2.5 of passage 4. Furthermore, the system 100ensures that the trajectory of another rolling stock, for example trainB, is not affected.

In one embodiment of the present invention, the system 100 may alsodetermine that a particular rolling stock may be less wearing if passingthrough a track with a certain speed, for example, passing through track2, while other similar train type usually runs through track 1. Thisapproach may be advantageous, as it may allow to reduce wear of trackand/or switch by evaluating and selecting the optimal route for thetrains based on their punctual circulation properties. Furthermore, thesystem 100 may predict a future status of the railway network and basedon that may determine an optimal routing of rolling stocks using dataanalysis based on at least one analytical approach, each approachcomprising at least one of signal filter processing, patternrecognition, probabilistic modeling, Bayesian schemes, machine learning,supervised learning, unsupervised learning, reinforcement learning,statistical analytics, statistical models, principle component analysis,independent component analysis (ICA), dynamic time warping, maximumlikelihood estimates, modeling, estimating, neural network,convolutional network, deep convolutional network, deep learning,ultra-deep learning, genetic algorithms, Markov models, and/or hiddenMarkov models.

For instance, when a rolling stock, for example train A, is approachinga switch, for example switch 1.4, which has been recently maintained,and on the other hand, another switch, for example switch 1.5, is closerto reach its maintenance cycle, the system 100 may determine to keeptrain A on track 1 until the rolling stock reaches the switch with thecloser maintenance cycle to be execute. Subsequently, the system 100 mayreroute the train A to track 2 through switch 1.5, instead of switch1.4. Such an approach may be advantageous, as it may allow to maximizethe life cycle of assets. In other words, it may allow the optimal useof railways considering the assets' health status, maintenance and/orinspections plans.

Furthermore, the system 100 may also be able of determining which routesmust be kept to ensure the optimal performance of tracks and the trafficof railways. In more simple words, the system 100 may be capable toidentify, based on the current condition of the switch engine, forexample, of switch 2.4, if the conditions are optimal for retaining theposition of the switch, i.e. if the conditions are the best to not movethe switch. As a result, the system 100 may be able to determine if theroutes of all coming rolling stocks through a section of the network,for example, switch 2.4, should either be kept in one a particularposition. Additionally, the system 100 may be able to identify how longthe routes must be kept based on the future conditions, i.e. the system100 may maintain the route of a rolling stock unaltered as long as theconditions relevant to the railway (e.g. wear effect, speed) guaranteesthe optimal routing for a traffic status, or if the conditions makes itnecessary the routes to be kept unchanged.

In more simple words, determinations of the system 100 may directly beused to control the traffic in railways as well as taking intoconsideration other rules of traffic control, such as, for example, butnot limited to, stops at stations, speed limits and safety regulations.Additionally, the determinations of the system 100 may also becommunicated to a common traffic control system, which may further takethe data into consideration when controlling the traffic in a pluralityof railway systems. Such a route planning may take into accounting pastand current information relevant to railway systems, and the analysisfor predicting future status may be based on at least one analyticalapproach, each approach comprising at least one of signal filterprocessing, pattern recognition, probabilistic modeling, Bayesianschemes, machine learning, supervised learning, unsupervised learning,reinforcement learning, statistical analytics, statistical models,principle component analysis, independent component analysis (ICA),dynamic time warping, maximum likelihood estimates, modeling,estimating, neural network, convolutional network, deep convolutionalnetwork, deep learning, ultra-deep learning, genetic algorithms, Markovmodels, and/or hidden Markov models.

While in the above, a preferred embodiment has been described withreference to the accompanying drawings, the skilled person willunderstand that this embodiment was provided for illustrative purposeonly and should by no means be construed to limit the scope of thepresent invention, which is defined by the claims.

Whenever a relative term, such as “about”, “substantially” or“approximately” is used in this specification, such a term should alsobe construed to also include the exact term. That is, e.g.,“substantially straight” should be construed to also include “(exactly)straight”.

Whenever steps were recited in the above or also in the appended claims,it should be noted that the order in which the steps are recited in thistext may be accidental. That is, unless otherwise specified or unlessclear to the skilled person, the order in which steps are recited may beaccidental. That is, when the present document states, e.g., that amethod comprises steps (A) and (B), this does not necessarily mean thatstep (A) precedes step (B), but it is also possible that step (A) isperformed (at least partly) simultaneously with step (B) or that step(B) precedes step (A). Furthermore, when a step (X) is said to precedeanother step (Z), this does not imply that there is no step betweensteps (X) and (Z). That is, step (X) preceding step (Z) encompasses thesituation that step (X) is performed directly before step (Z), but alsothe situation that (X) is performed before one or more steps (Y1), . . ., followed by step (Z). Corresponding considerations apply when termslike “after” or “before” are used.

1. A method for controlling traffic in railways, the method comprisingsampling sensor data relevant to railway system via at least one sensor;using at least one server for receiving the sensor data from the atleast one sensor; predicting the status of the railway infrastructurebased on future rolling stock; and controlling the traffic of rollingstock on the basis of the future status.
 2. The method according toclaim 1 further comprising processing sensor data to generate processedsensor data; analyzing the processed sensor data to obtain informationrelevant to railway system; using the obtained relevant information forplanning the routing of rolling stocks; and transmitting the routeplanning to at least one of the at least one server and/or at least oneauthorized user.
 3. The method according to claim 2 wherein routing ofrolling stocks is based on at least one of the following current orpredicted relevant information of railways: technical condition ofassets; degrading effect of rolling stocks; degrading effect of assets;traffic load information of rolling stocks; risks of traffic delay;unplanned maintenance and/or inspections; planned maintenance and/orinspections; maintenance effectiveness metrics; and weather information.4. The method according to claim 2 wherein the analysis of relevantinformation obtained from the sensor data and/or the routing of rollingstocks are based on at least one analytical approach.
 5. The methodaccording to claim 4 wherein the method further comprises associatingand/or arranging at least one sensor with at least one of rolling stockand/or railway infrastructure.
 6. The method according to claim 5wherein the method comprises using the at least one server providing atleast one signal comprising parameters to define the route of rollingstocks; prediction of railway traffic; prediction of wear effect ofrolling stocks on the railway infrastructure; contrast of the routeplanning with current traffic in railways; provision of feedback ofcurrent traffic in railways; provision of instruction for (semi)automatically controlling the traffic in railways; provision ofinstruction for (semi) automatically routing rolling stocks in railways;and wherein the at least one signal is based on at least one analyticalapproach.
 7. A system for controlling traffic in railways, the systemcomprising at least one sensor configured to sample sensor data relevantto railway system; at least one server configured to receive the sensordata from the sensor; predict the future status of the railwayinfrastructure based on future rolling stock; and control the traffic ofrolling stock on the basis of the future status.
 8. The system accordingto the claim 7, the system further comprising at least one sensor dataprocessing component configured to generate processed sensor data; atleast one analyzing component configured to analyze the processed sensordata to generate a rolling stock routing plan; at least one transmittingcomponent configured to transmit the route planning to at least oneserver and/or at least one authorized user through an interface.
 9. Themethod according to claim 9 wherein the at least one analyzing component(400) generates a rolling stock routing plan based on at least one ofthe following current or predicted relevant information of railways:technical condition of assets; degrading effect of rolling stocks;degrading effect of assets; traffic load information of rolling stocks;risks of traffic delay; unplanned maintenance and/or inspections;planned maintenance and/or inspections; maintenance effectivenessmetrics; and weather information.
 10. The system according to claim 8wherein the at least one analyzing component and/or the at least oneserver comprises optimizing the routing of rolling stocks based on atleast one analytical approach.
 11. The system according to claim 10further comprising the association and/or arrangement at least onesensor with at least one of rolling stock and/or railway infrastructure.12. The system according to claim 11 wherein the information sampled viaat least one sensor provide information of at least one sensor datameasurements.
 13. The system according to claim 8 wherein the at leastone server is configured to provide at least one signal comprisingparameters to define the route of rolling stocks; monitoring of trafficof rolling stocks considering the future status of railwayinfrastructure; prediction of railway traffic considering the futurestatus of railway infrastructure; prediction of wear effect of rollingstocks on the railway infrastructure; and wherein the at least onesignal is based on at least one analytical.
 14. The system according toclaim 11 wherein the at least one sensor is configured to perform in aplurality of operation modes, and wherein the operation modes can beconfigured to monitor a plurality of sensor data relevant to railwaysystem.
 15. The system according to claim 8 wherein the server comprisesan interface component configured to bidirectionally communicate the atleast one server with at least one authorized user.